Quantitative Estimation of Organic Matter Content in Arid Soil Using Vis-NIR Spectroscopy Preprocessed by Fractional Derivative

Wang, Jingzhe; Tiyip, Tashpolat; Ding, Jianli; Zhang, Dong; Liu, Wei; Wang, Fei

doi:10.1155/2017/1375158

Cited by 23 publications

(17 citation statements)

References 51 publications

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“…The application of derivatives is an ideal treatment in spectral analysis ( Viscarra Rossel, McGlynn & McBratney, 2006 ). The VIS–NIR reflectance was derivative-converted to reduce the influence of noise, amplify and reveal the greater spectral features ( Wang et al, 2017a ). The fractional derivative has expanded the theoretical concept of classic derivative, which has been successfully adopted in spectral data processing.…”

Section: Methodsmentioning

confidence: 99%

“…However, the detection of spectral information via FD and SD with wider order intervals could, to some degree, result in the loss of spectral information. Some studies have demonstrated significant improvements on potential applications of the fractional derivative in various fields ( Chen, 2008 ; Wang et al, 2017a ; Wang et al, 2017c ; Zhang et al, 2016a ). With the narrower order interval, the fractional derivative expanded the theoretical concept of classic derivative.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS–NIR) spectroscopy, Ebinur Lake Wetland, Northwest China

Wang

Ding

Abulimiti

et al. 2018

PeerJ

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Soil salinization is one of the most common forms of land degradation. The detection and assessment of soil salinity is critical for the prevention of environmental deterioration especially in arid and semi-arid areas. This study introduced the fractional derivative in the pretreatment of visible and near infrared (VIS–NIR) spectroscopy. The soil samples (n = 400) collected from the Ebinur Lake Wetland, Xinjiang Uyghur Autonomous Region (XUAR), China, were used as the dataset. After measuring the spectral reflectance and salinity in the laboratory, the raw spectral reflectance was preprocessed by means of the absorbance and the fractional derivative order in the range of 0.0–2.0 order with an interval of 0.1. Two different modeling methods, namely, partial least squares regression (PLSR) and random forest (RF) with preprocessed reflectance were used for quantifying soil salinity. The results showed that more spectral characteristics were refined for the spectrum reflectance treated via fractional derivative. The validation accuracies showed that RF models performed better than those of PLSR. The most effective model was established based on RF with the 1.5 order derivative of absorbance with the optimal values of R2 (0.93), RMSE (4.57 dS m−1), and RPD (2.78 ≥ 2.50). The developed RF model was stable and accurate in the application of spectral reflectance for determining the soil salinity of the Ebinur Lake wetland. The pretreatment of fractional derivative could be useful for monitoring multiple soil parameters with higher accuracy, which could effectively help to analyze the soil salinity.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS–NIR) spectroscopy, Ebinur Lake Wetland, Northwest China

Wang

Ding

Abulimiti

et al. 2018

PeerJ

Self Cite

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“…The SOM content in the Ebinur Lake basin is low (near 2%). With SOM content of 2% as a boundary, that is, when SOM content exceeded 2%, the SOM played a principal role in masking out the spectral features, while the SOM content was less than 2%, it became less effective [ 46 , 52 , 53 ]. In the study, the soil clay content was divided into five groups: 0%–1% ( n = 46), 1%–2% ( n = 40), 2%–3% ( n = 8), 3%–4% ( n = 7), and >4% ( n = 2).…”

Section: Discussionmentioning

confidence: 99%

Desert soil clay content estimation using reflectance spectroscopy preprocessed by fractional derivative

et al. 2017

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Effective pretreatment of spectral reflectance is vital to model accuracy in soil parameter estimation. However, the classic integer derivative has some disadvantages, including spectral information loss and the introduction of high-frequency noise. In this paper, the fractional order derivative algorithm was applied to the pretreatment and partial least squares regression (PLSR) was used to assess the clay content of desert soils. Overall, 103 soil samples were collected from the Ebinur Lake basin in the Xinjiang Uighur Autonomous Region of China, and used as data sets for calibration and validation. Following laboratory measurements of spectral reflectance and clay content, the raw spectral reflectance and absorbance data were treated using the fractional derivative order from the 0.0 to the 2.0 order (order interval: 0.2). The ratio of performance to deviation (RPD), determinant coefficients of calibration (), root mean square errors of calibration (RMSEC), determinant coefficients of prediction (), and root mean square errors of prediction (RMSEP) were applied to assess the performance of predicting models. The results showed that models built on the fractional derivative order performed better than when using the classic integer derivative. Comparison of the predictive effects of 22 models for estimating clay content, calibrated by PLSR, showed that those models based on the fractional derivative 1.8 order of spectral reflectance ( = 0.907, RMSEC = 0.425%, = 0.916, RMSEP = 0.364%, and RPD = 2.484 ≥ 2.000) and absorbance ( = 0.888, RMSEC = 0.446%, = 0.918, RMSEP = 0.383% and RPD = 2.511 ≥ 2.000) were most effective. Furthermore, they performed well in quantitative estimations of the clay content of soils in the study area.

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“…To exploit the potential information in the spectral data, the Grünwald-Letnikov's Fractional-Order Differential Equations (FDEs) with FOs ranging from 0 to 2 and stepped by 0.2 were also applied on the raw reflectance and the transformed data in this study. Wang et al [14] measured the visible and near-infrared (Vis-NIR) spectroscopy and soil organic matter (SOM) content of 103 soil samples collected in the Ebinur Lake basin, Xinjiang Uighur Autonomous Region, China, and employed the fractional derivative algorithm for pretreatment. Partial least squares regression (PLSR) was applied for model calibration in their study.…”

Section: Introductionmentioning

confidence: 99%

Geographically Weighted Regression Effects on Soil Zinc Content Hyperspectral Modeling by Applying the Fractional-Order Differential

Shang

et al. 2019

Remote Sensing

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With the development of remote sensing techniques and the increasing need for soil contamination monitoring, we estimated soil heavy metal zinc (Zn) content using hyperspectral imaging. Geographically weighted regression (GWR), an extension of the ordinary least squares (OLS) regression framework, was proposed. By estimating a set of parameters for any number of locations in a study area, GWR can probe the spatial heterogeneity in data relationships, whereas the regression parameters of an OLS model are global and aspatially-varied. The objectives of this study were: (1) To find the possible relationships between hyperspectral data and soil Zn content, and (2) to investigate the existence of their spatial heterogeneity. In this study, 67 soil samples collected from Pingtan Island, Fujian Province, China, were used to conduct laboratory hyperspectral modeling for soil Zn content estimation. Four transformations of square root, logarithm, reciprocal of logarithm, and reciprocal, as well as the fractional-order differential operations were applied to increase the amount of reflectance data in which the effective variables for modeling might be involved, and to enhance the spectral characteristics of soil Zn content. To find sensitive variables and to remove redundancy and multicollinearity in the spectra, a data sifting process was applied by selecting wavelengths with local maximum in the absolute values of the correlation coefficients with Zn content in one type of spectral data and by employing Variance Inflation Factors. Since a modeling sample size of 46 is insufficient to construct the appropriate OLS and GWR models, four methods are proposed using all 67 samples to choose explanatory variables. A random process to select 57 samples for modeling and 10 samples for validation was applied to assess model performance, in which the mean verification R2 (Rv2) was used as an indicator. The results show that GWR stepwise regression is the most effective method to select better variables. As the mean Rv2 converges toward the OLS value when the bandwidth of the GWR model increases, the four variables selected by the GWR stepwise regression were used to establish the representative OLS and GWR models. The representative OLS model has the best mean verification effect among all studied models, which had a mean Rv2 value that is 44.6% higher than the OLS model constructed using OLS stepwise regression.

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Quantitative Estimation of Organic Matter Content in Arid Soil Using Vis-NIR Spectroscopy Preprocessed by Fractional Derivative

Cited by 23 publications

References 51 publications

Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS–NIR) spectroscopy, Ebinur Lake Wetland, Northwest China

Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS–NIR) spectroscopy, Ebinur Lake Wetland, Northwest China

Desert soil clay content estimation using reflectance spectroscopy preprocessed by fractional derivative

Geographically Weighted Regression Effects on Soil Zinc Content Hyperspectral Modeling by Applying the Fractional-Order Differential

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